Towards voice based prediction and analysis of emotions in ASD children

Abstract

Voice processing has proven to be an eminent way of recognizing the various emotions of the people. The objective of this research is to identify the presence of Autism Spectrum Disorder (ASD) and to analyze the emotions of autistic children through their voices. The presented automated voice-based system can detect and classify seven basic emotions (anger, disgust, neutral, happiness, calmness, fear and sadness) expressed by children through source parameters associated with their voices. Various prime voice features such as Mel-frequency Cepstral Coefficients (MFCC) and Spectrogram are extracted and utilized to train a Multi-layer Perceptron (MLP) Classifier to identify possible emotions exhibited by the children thereby assessing their behavioral state. This proposed work therefore helps in the examination of emotions in autistic children that can be used to assess the kind of training and care required to enhance their lifestyle.